{ "id": "epmjc8fv0j", "slug": "mirna-target-prediction", "title": "miRNA target prediction", "updated": "2026-10-15", "topic_path": [ { "id": "life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/topics/life" }, { "id": "life.biology", "label": "Biological foundations", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology" }, { "id": "life.biology.rna", "label": "RNA and gene regulation", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.rna" }, { "id": "life.biology.rna.small-regulatory-rnas", "label": "Small regulatory RNAs", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.rna.small-regulatory-rnas" }, { "id": "life.biology.rna.mirna-biology", "label": "microRNA biology", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.rna.mirna-biology" }, { "id": "life.biology.rna.mirna-biology.databases-and-computation", "label": "miRNA databases and computational prediction", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.rna.mirna-biology.databases-and-computation" } ], "geo": [ { "id": "geo.nongeo.t2001.life.biology.rna", "label": "Non-geographic · 2001 to 2020: RNA and gene regulation", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology.rna", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t2001", "label": "Non-geographic · 2001 to 2020", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001" }, { "id": "geo.nongeo.t2001.life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life" }, { "id": "geo.nongeo.t2001.life.biology", "label": "Biological foundations", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology" }, { "id": "geo.nongeo.t2001.life.biology.rna", "label": "RNA and gene regulation", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology.rna" } ] } ], "excerpt": "miRNA target prediction is the set of computational methods that predict which messenger RNAs are regulated by microRNAs, mostly by searching for seed-region pairing and scoring candidate sites.", "snippet": "miRNA target prediction is the set of computational methods that predict which messenger RNAs are regulated by microRNAs, mostly by searching for seed-region pairing and scoring candidate sites.", "node": "life.biology.rna.mirna-biology.databases-and-computation", "markdown": "# miRNA target prediction\n\nmiRNA target prediction is a set of computational methods that predict which messenger RNAs are regulated by microRNAs, almost always by searching for Watson-Crick pairing to the miRNA's seed region and scoring candidate sites with conservation, context, and energetic features. The output is a ranked list of miRNA–mRNA pairs with per-site and per-target scores, used downstream for functional annotation, pathway analysis, and experiment design. Early genome-wide analyses implicated more than 5,300 human genes, about 30% of the gene set studied, as conserved miRNA targets[1](https://doi.org/10.1016/j.cell.2004.12.035), and estimated that an average miRNA has approximately 100 evolutionarily conserved target sites.[2](https://journals.plos.org/plosbiology/article?id=10.1371%2Fjournal.pbio.0030085)\n\n| Key fact | Value |\n|---|---|\n| Core rule | Perfect seed match to miRNA nt 2–8 (TargetScan)[3](https://doi.org/10.1016/s0092-8674%2803%2901018-3) or nt 2–7 with an m8 or t1A anchor (TargetScanS)[1](https://doi.org/10.1016/j.cell.2004.12.035) |\n| Scale of regulation | >5,300 human genes (~30% of the gene set) with conserved seed matches; ~100 conserved sites per miRNA[1](https://doi.org/10.1016/j.cell.2004.12.035) • [2](https://journals.plos.org/plosbiology/article?id=10.1371%2Fjournal.pbio.0030085) |\n| Benchmark precision (pSILAC) | 44% for seed-only matching; ~62% for TargetScanS and PicTar; ~66% for DIANA-microT 3.0[4](https://link.springer.com/article/10.1186/1471-2105-10-295) |\n| Prediction counts per miRNA | miRanda 7,982 vs TargetScan 1,367 vs DIANA-microT 961 interactions[5](https://www.mdpi.com/1422-0067/17/12/1987) |\n| CLIP comparison | 7-mer and 8-mer seeds are found in only about half of AGO binding sites[6](https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1005026) |\n\n## How it works\n\nThe central feature is seed pairing. TargetScan, reported by Lewis, Shih, Jones-Rhoades, Bartel, and Burge in Cell in 2003, defines the seed as bases 2–8 from the miRNA 5′ end and searches UTRs for perfect Watson-Crick matches, extending them with G:U-allowed pairing and scoring the duplex with RNAfold.[3](https://doi.org/10.1016/s0092-8674%2803%2901018-3) The simplified TargetScanS algorithm of Lewis, Burge, and Bartel (Cell, 2005) instead requires a conserved 6-nt match to nt 2–7 flanked by either an m8 match (nt 8) or a t1A adenosine anchor opposite miRNA nt 1; adding the t1A anchor raised the signal:noise ratio to 3.8:1 at a 51% cost in sensitivity.[1](https://doi.org/10.1016/j.cell.2004.12.035) Experimental work by Brennecke, Stark, Russell, and Cohen showed that as few as four base pairs at positions 2–5 can confer regulation, explaining why the 5′ seed dominates recognition.[2](https://journals.plos.org/plosbiology/article?id=10.1371%2Fjournal.pbio.0030085)\n\nCanonical site types are the 6mer (nt 2–7), 7mer-A1, 7mer-m8, and 8mer.[7](https://academic.oup.com/bib/article/16/5/780/217102) Because a 7-nt motif occurs frequently by chance, effective predictors add context: the context scores of Grimson, Farh, Johnston, Garrett-Engele, Lim, and Bartel (Molecular Cell, 2007) and later work weight local AU content, site position in the 3′UTR, 3′-pairing at nt 12–17, and nearby sites of co-expressed miRNAs.[5](https://www.mdpi.com/1422-0067/17/12/1987) • [8](https://doi.org/10.1016/j.molcel.2007.06.017) The context++ model of Agarwal, Bell, Nam, and Bartel (eLife, 2015) evaluates site type plus 14 features selected by stepwise regression from 26 candidates over 74 pre-processed high-throughput datasets, and performed better than the previous best model, TargetScan6.All.[9](https://doi.org/10.7554/elife.05005) Friedman, Farh, Burge, and Bartel (Genome Research, 2008) added the probability of conserved targeting (PCT), a phylogenetic branch-length measure over 28 vertebrate genomes.[10](https://doi.org/10.1101/gr.082701.108) • [5](https://www.mdpi.com/1422-0067/17/12/1987)\n\n## How it is done\n\nA typical run takes a miRNA (or a set from miRBase) and 3′UTR sequences, chooses a tool, and applies a score threshold. In TargetScan, predictions are found by searching for conserved 8mer, 7mer, and 6mer sites matching the seed; Release 8 ranks mammalian predictions by default with a biochemical model of repression extended to all miRNAs using a convolutional neural network, with cumulative weighted context++ scores or PCT as alternatives.[11](https://www.targetscan.org/vert_80/) • [12](https://doi.org/10.1126/science.aav1741) The documentation advises thresholding at the target level, using cumulative weighted context scores or aggregate PCT, because multiple weak sites can add up to more repression than a single strong site.[13](https://www.targetscan.org/faqs.Release_7.html) Predictions are then filtered, often by conservation or expression co-preservation, and checked against experimentally supported target databases before wet-lab validation.\n\n## Origin\n\nA 2003 [Drosophila](https://www.edgepedia.org/drosophila) target predictor by Stark, Brennecke, Russell and Cohen in PLoS Biology is described in a later review as the first miRNA target predictor.[14](https://doi.org/10.1371/journal.pbio.0000060) • [7](https://academic.oup.com/bib/article/16/5/780/217102) The 2003–2005 wave followed: TargetScan (Lewis et al., Cell, 2003)[3](https://doi.org/10.1016/s0092-8674%2803%2901018-3), the duplex predictor RNAhybrid (Rehmsmeier, Steffen, Höchsmann and Giegerich, RNA, 2004)[15](https://doi.org/10.1261/rna.5248604), a method by Rajewsky and Socci (Developmental Biology, 2004) that recovered known target sites with high specificity[16](https://doi.org/10.1016/j.ydbio.2003.12.003), TargetScanS (Lewis, Burge and Bartel, Cell, 2005)[1](https://doi.org/10.1016/j.cell.2004.12.035), and the combinatorial predictor PicTar (Krek, Grün, Poy and colleagues, [Nature Genetics](https://www.edgepedia.org/nature-genetics), 2005).[17](https://doi.org/10.1038/ng1536) Later refinements include mirSVR (Betel, Koppal, Agius, Sander and Leslie, Genome Biology, 2010)[18](https://doi.org/10.1186/gb-2010-11-8-r90) and the DIANA-microT-ANN neural-network predictor (Reczko, Maragkakis, Alexiou, Papadopoulos, and Hatzigeorgiou, Frontiers in Genetics, 2012) with its CDS-scoring successor DIANA-microT-CDS (Reczko, Maragkakis, Alexiou, Grosse, and Hatzigeorgiou, Bioinformatics, 2012).[19](https://doi.org/10.3389/fgene.2011.00103) • [20](https://doi.org/10.1093/bioinformatics/bts043)\n\n## Variants\n\nA review of the field identifies four features underlying most predictors: seed match, conservation, free energy, and site accessibility.[21](https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2014.00023/full) The tools differ mainly in which they emphasize.\n\n**miRanda** scores complementarity along the entire miRNA with a Smith-Waterman-like dynamic programming alignment (G:U wobbles allowed but scored lower), keeps hits with alignment score \\( S \\ge 80 \\) and duplex energy \\( \\Delta G \\le -14 \\) kcal/mol, computes energy with the Vienna RNA package[22](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2003-5-1-r1.pdf) • [23](https://doi.org/10.1007/bf00818163), and can add a shuffled-sequence Z-score filter.[24](https://www.animalgenome.org/bioinfo/resources/manuals/miranda.html) **PITA** uses accessibility as its major feature, scoring \\( \\Delta\\Delta G = \\Delta G_{\\mathrm{duplex}} - \\Delta G_{\\mathrm{open}} \\), where \\( \\Delta G_{\\mathrm{open}} \\) is the energy to unpair the target region computed with RNAfold over a 70-nt window on each side.[25](https://www.mdpi.com/2073-4425/14/3/664) **DIANA-microT** classifies miRNA recognition element types, scores CDS as well as 3′UTR sites, and treats conservation as a feature rather than a filter.[21](https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2014.00023/full)\n\nThe sensitivity/specificity trade-off is large: for one determined miRNA, miRanda predicts 7,982 interactions versus 1,367 for TargetScan and 961 for DIANA-microT; TargetScan is described as the most precise sequence-based tool but with a high false-negative rate, while miRanda is more sensitive with more false positives.[5](https://www.mdpi.com/1422-0067/17/12/1987) Consensus offers little measured gain: the union of TargetScan 5.0 and DIANA-microT-ANN achieved the same precision and only 3% higher sensitivity than DIANA-microT-ANN alone.[19](https://doi.org/10.3389/fgene.2011.00103) Recent work adds deep learning, with predecessors including miRAW (Pla, Zhong and Rayner, PLOS Computational Biology, 2018)[26](https://doi.org/10.1371/journal.pcbi.1006185) and TarPmiR (Ding, Li and Hu, Bioinformatics, 2016)[27](https://doi.org/10.1093/bioinformatics/btw318); DIANA-microT 2023 hosts more than 86 million miRNA–gene interactions with exact miRNA recognition element locations, including about 2.5 million predictions for virally encoded miRNAs.[28](https://doi.org/10.1093/nar/gkad283) • [29](https://dianalab.e-ce.uth.gr/microt_webserver/)\n\n## Applications\n\nPredictions feed functional annotation and pathway analysis, and web servers combine them with experimental support databases and expression data; DIANA-microT 2023, for example, integrates miRNA abundance estimates for 60 tissues and 210 cell lines with GTEx and TCGA gene expression and SNPs overlapping recognition elements.[29](https://dianalab.e-ce.uth.gr/microt_webserver/) Because roughly half of one tool's targets can be unique to that tool[4](https://link.springer.com/article/10.1186/1471-2105-10-295), downstream users typically treat predictions as candidate lists for prioritization rather than as validated regulatory maps.\n\n## Limitations and alternatives\n\nShort seed motifs occur frequently in the transcriptome and are not sufficient to predict binding, producing high false discovery rates for purely bioinformatic predictions.[30](https://www.nature.com/articles/ncomms9864) Published false-positive estimates for conserved sites of some programs are close to 50%, and for Hominidae-specific seeds the rate for microT and miRanda appears to approach 50% or 70%, partly because some conserved seed matches are conserved for miRNA-independent reasons.[31](https://pmc.ncbi.nlm.nih.gov/articles/PMC5287229/)\n\nOn the Selbach pSILAC benchmark (five miRNAs over-expressed in HeLa cells, measuring the fraction of predicted targets actually downregulated), simple seed matching achieved 44% precision, TargetScanS and PicTar about 62%, and DIANA-microT 3.0 about 66%.[4](https://link.springer.com/article/10.1186/1471-2105-10-295)\n\nCross-linking methods give a different picture. CLEAR-CLIP, reported by Moore, Scheel, Luna and colleagues in [Nature Communications](https://www.edgepedia.org/nature-communications) in 2015, mapped about 130,000 endogenous miRNA–target interactions in mouse brain and about 40,000 in human hepatoma cells.[30](https://www.nature.com/articles/ncomms9864) TargetScan supported only a minority of chimera-defined sites, a major discrepancy being the preponderance of 6mer and imperfect seed-match variants absent from TargetScan.[30](https://www.nature.com/articles/ncomms9864) Experimental mapping has its own limits: 7-mer and 8-mer seeds appear in only about half of AGO binding sites, and CLASH-style protocols capture chimeric reads at only about 2% efficiency, so many interactions remain uncaptured.[6](https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1005026) [Supervised learning](https://www.edgepedia.org/supervised-learning) on CLIP data narrows the gap: chimiRic, trained on AGO CLIP and CLASH data by Lu and Leslie, outperformed TargetScan and mirSVR by precision-recall area on held-out seed families.[6](https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1005026)\n\n## References\n\n1. [Benjamin P. Lewis, Christopher B. Burge, David P. Bartel (2005). Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets. Cell.](https://doi.org/10.1016/j.cell.2004.12.035)\n2. [Principles of MicroRNA–Target Recognition](https://journals.plos.org/plosbiology/article?id=10.1371%2Fjournal.pbio.0030085)\n3. [Prediction of Mammalian MicroRNA Targets (Cell, 2003)](https://doi.org/10.1016/s0092-8674%2803%2901018-3)\n4. [Accurate microRNA target prediction correlates with protein repression levels (DIANA-microT 3.0)](https://link.springer.com/article/10.1186/1471-2105-10-295)\n5. [Tools for Sequence-Based miRNA Target Prediction: What to Choose?](https://www.mdpi.com/1422-0067/17/12/1987)\n6. [Learning to Predict miRNA-mRNA Interactions from AGO CLIP Sequencing and CLASH Data (chimiRic)](https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1005026)\n7. [Comprehensive overview and assessment of computational prediction of microRNA targets in animals](https://academic.oup.com/bib/article/16/5/780/217102)\n8. [Andrew Grimson and colleagues (2007). MicroRNA Targeting Specificity in Mammals: Determinants beyond Seed Pairing. Molecular Cell.](https://doi.org/10.1016/j.molcel.2007.06.017)\n9. [Vikram Agarwal and colleagues (2015). Predicting effective microRNA target sites in mammalian mRNAs. eLife.](https://doi.org/10.7554/elife.05005)\n10. [Robin C. Friedman and colleagues (2008). Most mammalian mRNAs are conserved targets of microRNAs. Genome Research.](https://doi.org/10.1101/gr.082701.108)\n11. [TargetScanHuman 8.0 (official database home page)](https://www.targetscan.org/vert_80/)\n12. [Sean E. McGeary and colleagues (2019). The biochemical basis of microRNA targeting efficacy. Science.](https://doi.org/10.1126/science.aav1741)\n13. [TargetScan 7 Frequently Asked Questions](https://www.targetscan.org/faqs.Release_7.html)\n14. [Alexander Stark and colleagues (2003). Identification of Drosophila MicroRNA Targets. PLoS Biology.](https://doi.org/10.1371/journal.pbio.0000060)\n15. [MARC REHMSMEIER and colleagues (2004). Fast and effective prediction of microRNA/target duplexes. RNA.](https://doi.org/10.1261/rna.5248604)\n16. [Nikolaus Rajewsky, Nicholas D Socci (2004). Computational identification of microRNA targets. Developmental Biology.](https://doi.org/10.1016/j.ydbio.2003.12.003)\n17. [Azra Krek and colleagues (2005). Combinatorial microRNA target predictions. Nature Genetics.](https://doi.org/10.1038/ng1536)\n18. [Doron Betel and colleagues (2010). Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome biology.](https://doi.org/10.1186/gb-2010-11-8-r90)\n19. [Martin Reczko and colleagues (2012). Accurate microRNA Target Prediction Using Detailed Binding Site Accessibility and Machine Learning on Proteomics Data. Frontiers in Genetics.](https://doi.org/10.3389/fgene.2011.00103)\n20. [Martin Reczko and colleagues (2012). Functional microRNA targets in protein coding sequences. Bioinformatics.](https://doi.org/10.1093/bioinformatics/bts043)\n21. [Common features of microRNA target prediction tools](https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2014.00023/full)\n22. [MicroRNA targets in Drosophila (miRanda)](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2003-5-1-r1.pdf)\n23. [I. L. Hofacker and colleagues (1994). Fast folding and comparison of RNA secondary structures. Monatshefte für Chemie - Chemical Monthly.](https://doi.org/10.1007/bf00818163)\n24. [miranda(l) - The miRanda Package manual](https://www.animalgenome.org/bioinfo/resources/manuals/miranda.html)\n25. [MicroRNA Target Identification: Revisiting Accessibility and Seed Anchoring](https://www.mdpi.com/2073-4425/14/3/664)\n26. [Albert Pla, Xiangfu Zhong, Simon Rayner (2018). miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts. PLoS Computational Biology.](https://doi.org/10.1371/journal.pcbi.1006185)\n27. [Jun Ding, Xiaoman Li, Haiyan Hu (2016). TarPmiR: a new approach for microRNA target site prediction. Bioinformatics.](https://doi.org/10.1093/bioinformatics/btw318)\n28. [Spyros Tastsoglou and colleagues (2023). DIANA-microT 2023: including predicted targets of virally encoded miRNAs. Nucleic Acids Research.](https://doi.org/10.1093/nar/gkad283)\n29. [DIANA-microT 2023 webserver](https://dianalab.e-ce.uth.gr/microt_webserver/)\n30. [miRNA–target chimeras reveal miRNA 3′-end pairing as a major determinant of Argonaute target specificity (CLEAR-CLIP)](https://www.nature.com/articles/ncomms9864)\n31. [microRNA target prediction programs predict many false positives](https://pmc.ncbi.nlm.nih.gov/articles/PMC5287229/)\n\n---\n*Topic: Encyclopedia › Life and health › Biological foundations › RNA and gene regulation › Small regulatory RNAs › microRNA biology › miRNA databases and computational prediction*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [], "url": "https://www.edgepedia.org/mirna-target-prediction", "markdown_url": "https://www.edgepedia.org/mirna-target-prediction.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.", "spdx": "LicenseRef-Edgepedia-Community-1.0" }, "credit": "\"miRNA target prediction\", Edgepedia (EdgeChat), https://www.edgepedia.org/mirna-target-prediction. Edgepedia Community License 1.0.", "credit_md": "\"[miRNA target prediction](https://www.edgepedia.org/mirna-target-prediction)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/mirna-target-prediction](https://www.edgepedia.org/mirna-target-prediction). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).", "credit_html": "\"miRNA target prediction\", Edgepedia (EdgeChat), https://www.edgepedia.org/mirna-target-prediction. Edgepedia Community License 1.0.", "speakable": "miRNA target prediction is the set of computational methods that predict which messenger RNAs are regulated by microRNAs, mostly by searching for seed-region pairing and scoring candidate sites." }